Abstract

In order to synthesize planar, sparse, and aperiodic arrays, a numerical procedure based on an enhanced genetic algorithm is proposed. The method maximizes a suitably defined single-objective fitness function iteratively acting on the states and the weights of the elements of the array. Such a cost function is related to the shape of the desired beam pattern, to the number of active elements and to others user-defined array-pattern constraints. To preliminarily assess the effectiveness of the approach, selected numerical experiments are performed. The obtained results seem to confirm its feasibility. Moreover, given the heterogeneity of the test benchmarks, the versatility is pointed out as a key-feature of the implemented methodology.